Conditions for validity of a robust-error-variance Poisson regression A variant of a Poisson regression called the "robust-error-variance Poisson regression" is an approach adapted for binary data, specially as an alternative to the logistic regression. What are the conditions of validity that are needed to apply a "robust error variance Poisson regression"? Or in other words, are there any number of assumptions that must be met before doing such a model?
References:
Guangyong Zou, A Modified Poisson Regression Approach to Prospective Studies with Binary Data, Am. J. Epidemiol. (2004) 159 (7): 702-706 doi:10.1093/aje/kwh090
 A: There is a useful discussion by Lumley and colleagues "Relative risk regression in medical research: models, contrasts, estimators, and algorithms" available here which despite the title is not exclusive to medical research.
As I understand it the main problem with the Poisson working model is that it is capable of giving predicted probabilities greater than 1. The other main competitor, the log-binomial does not do that. Whether you see this as a problem with the algorithm or a problem with using relative risks where the baseline risk is high is a matter for your decision I think.
A: To add to @mdewey's answer
The issue with maximum likelihood is that it constrains fitted probabilities to be at most 1, which can give x-outliers extremely high influence on the results. As a consequence, the estimator takes some effort to compute reliably, and its distribution might not be close to Normal
Any estimator that avoids the outlier-sensitivity of the MLE must do so by allowing fitted probabilities greater than 1, as the Poisson working model does. Reasonable people can disagree on whether that's a good tradeoff.
One important reason for picking the Poisson working model over other alternatives to the MLE (such as nonlinear least squares) is partly aesthetic: it approximates the MLE well when the fitted probabilities are small.  This allows the same estimator to be used for rare and common events. 
